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<title>Compute Library: tests/validation/reference/FullyConnectedLayer.cpp Source File</title>
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<div class="title">FullyConnectedLayer.cpp</div>  </div>
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<a href="validation_2reference_2_fully_connected_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_fully_connected_layer_8h.xhtml">FullyConnectedLayer.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2_fixed_point_8h.xhtml">tests/validation/FixedPoint.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_utils_quantized_asymm_8h.xhtml">tests/validation/reference/UtilsQuantizedAsymm.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>&quot;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &lt;numeric&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="keyword">namespace </span>validation</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="keyword">namespace </span>reference</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;{</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;{</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="comment">// Vector matrix multiply for floating point</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;template &lt; typename T, typename TB, typename std::enable_if &lt; is_floating_point&lt;T&gt;::value &amp;&amp;is_floating_point&lt;TB&gt;::value, <span class="keywordtype">int</span> &gt;::type = 0 &gt;</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor&lt;T&gt; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <span class="keyword">const</span> SimpleTensor&lt;T&gt; &amp;weights, <span class="keyword">const</span> SimpleTensor&lt;TB&gt; &amp;bias, SimpleTensor&lt;T&gt; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst, <span class="keywordtype">int</span> cols_weights,</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;                            <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;{</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(fixed_point_position);</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    <span class="keyword">const</span> T *src_ptr     = src.data() + offset_src;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <span class="keyword">const</span> T *weights_ptr = weights.data();</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    <span class="keyword">const</span> TB *bias_ptr    = bias.data();</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    T        *dst_ptr     = dst.data() + offset_dst;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y &lt; rows_weights; ++y)</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    {</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast&lt;T&gt;(0)) + bias_ptr[y];</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;        weights_ptr += cols_weights;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    }</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;}</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="comment">// Vector matrix multiply for fixed point type</span></div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;template &lt; typename T, typename TB, typename std::enable_if &lt; std::is_integral&lt;T&gt;::value &amp;&amp;std::is_integral&lt;TB&gt;::value, <span class="keywordtype">int</span> &gt;::type = 0 &gt;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor&lt;T&gt; &amp;src, <span class="keyword">const</span> SimpleTensor&lt;T&gt; &amp;weights, <span class="keyword">const</span> SimpleTensor&lt;TB&gt; &amp;bias, SimpleTensor&lt;T&gt; &amp;dst, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst, <span class="keywordtype">int</span> cols_weights,</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;                            <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;{</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="keyword">const</span> T *src_ptr     = src.data() + offset_src;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <span class="keyword">const</span> T *weights_ptr = weights.data();</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    <span class="keyword">const</span> TB *bias_ptr    = bias.data();</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    T        *dst_ptr     = dst.data() + offset_dst;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="keyword">using namespace </span>fixed_point_arithmetic;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keyword">using</span> promoted_type = fixed_point_arithmetic::traits::promote_t&lt;T&gt;;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y &lt; rows_weights; ++y)</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    {</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        <span class="comment">// Reset accumulator</span></div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        fixed_point&lt;promoted_type&gt; acc(0, fixed_point_position);</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x &lt; cols_weights; ++x)</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        {</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;            <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; i_value(src_ptr[x], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;            <span class="keyword">const</span> fixed_point&lt;promoted_type&gt; w_value(weights_ptr[x], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;            acc = acc + i_value * w_value;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        }</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        <span class="comment">// Get the bias</span></div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        <span class="keyword">const</span> fixed_point&lt;T&gt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>(bias_ptr[y], fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="comment">// Convert back and accumulate the bias</span></div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        fixed_point&lt;T&gt; res(acc);</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        res = res + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="comment">// Store the result</span></div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        dst_ptr[y] = res.raw();</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        weights_ptr += cols_weights;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    }</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;}</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;<span class="comment">// Vector matrix multiply for quantized type</span></div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;<span class="keyword">template</span> &lt;&gt;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;<span class="keywordtype">void</span> vector_matrix_multiply(<span class="keyword">const</span> SimpleTensor&lt;uint8_t&gt; &amp;src, <span class="keyword">const</span> SimpleTensor&lt;uint8_t&gt; &amp;weights, <span class="keyword">const</span> SimpleTensor&lt;int32_t&gt; &amp;bias, SimpleTensor&lt;uint8_t&gt; &amp;dst, <span class="keywordtype">int</span> offset_src, <span class="keywordtype">int</span> offset_dst,</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                            <span class="keywordtype">int</span> cols_weights, <span class="keywordtype">int</span> rows_weights, uint8_t fixed_point_position)</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;{</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(fixed_point_position);</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <span class="keyword">const</span> uint8_t *src_ptr     = src.data() + offset_src;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="keyword">const</span> uint8_t *weights_ptr = weights.data();</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keyword">const</span> int32_t *bias_ptr    = bias.data();</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    uint8_t       *dst_ptr     = dst.data() + offset_dst;</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>   input_offset   = -src.quantization_info().offset;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> input_scale    = src.quantization_info().scale;</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>   weights_offset = -weights.quantization_info().offset;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> weights_scale  = weights.quantization_info().scale;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>   output_offset  = dst.quantization_info().offset;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> output_scale   = dst.quantization_info().scale;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="keywordtype">int</span>         output_multiplier = 0;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="keywordtype">int</span>         output_shift      = 0;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier        = input_scale * weights_scale / output_scale;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <a class="code" href="namespacearm__compute_1_1quantization.xhtml#aa7bd9c3a3bcfe392c90d78e29429db26">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a>(multiplier, &amp;output_multiplier, &amp;output_shift);</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y &lt; rows_weights; ++y)</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    {</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;        <span class="comment">// Reset accumulator</span></div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        int32_t acc = 0;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x &lt; cols_weights; ++x)</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        {</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;            acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset);</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        }</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="comment">// Accumulate the bias</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        acc += bias_ptr[y];</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;        acc = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">asymm_rounding_divide_by_pow2</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">asymm_int_mult</a>(acc, output_multiplier), output_shift);</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        acc += output_offset;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        acc = utility::clamp&lt;int32_t&gt;(acc, 0, 255);</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="comment">// Store the result</span></div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        dst_ptr[y] = <span class="keyword">static_cast&lt;</span>uint8_t<span class="keyword">&gt;</span>(acc);</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        weights_ptr += cols_weights;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    }</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;}</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keyword">typename</span> TB&gt;</div><div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">  152</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;TB&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape)</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;{</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    <span class="comment">// Create reference</span></div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> dst{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>{ dst_shape }, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>(), src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>() };</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    <span class="comment">// Sanity checks</span></div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>          num_batch_dimensions = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(0, static_cast&lt;int&gt;(dst_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</a>()) - 1);</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span>          num_input_dimensions = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().num_dimensions() - num_batch_dimensions;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> linear_input_size    = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size_lower(num_input_dimensions);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(num_batch_dimensions);</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(num_input_dimensions);</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(linear_input_size);</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x() != linear_input_size);</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y() != bias.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x());</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y() != dst.shape().x());</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    <span class="comment">// Compute reference</span></div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> cols_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> rows_weights = weights.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches  = dst_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">total_size_upper</a>(1);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k &lt; num_batches; ++k)</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    {</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> offset_in  = k * cols_weights;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> offset_out = k * rows_weights;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        vector_matrix_multiply&lt;T&gt;(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>,</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;                                  weights,</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;                                  bias,</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;                                  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>,</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;                                  offset_in,</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;                                  offset_out,</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;                                  cols_weights,</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;                                  rows_weights,</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;                                  src.fixed_point_position());</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    }</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;}</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape);</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape);</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint8_t&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;qint16_t&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape);</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">fully_connected_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;src, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;uint8_t&gt;</a> &amp;weights, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;int32_t&gt;</a> &amp;bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;dst_shape);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;} <span class="comment">// namespace reference</span></div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;} <span class="comment">// namespace validation</span></div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5bab95cbeb5c6bf05049df7afd32d823"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5bab95cbeb5c6bf05049df7afd32d823">arm_compute::test::validation::asymm_rounding_divide_by_pow2</a></div><div class="ttdeci">int32_t asymm_rounding_divide_by_pow2(int32_t x, int exponent)</div><div class="ttdoc">Rounded to nearest division by a power-of-two. </div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00036">UtilsQuantizedAsymm.h:36</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00337">SimpleTensor.h:337</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_aa7bd9c3a3bcfe392c90d78e29429db26"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#aa7bd9c3a3bcfe392c90d78e29429db26">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(double multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one. </div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00294">SimpleTensor.h:294</a></div></div>
<div class="ttc" id="tests_2validation_2_fixed_point_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_fixed_point_8h.xhtml">FixedPoint.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
<div class="ttc" id="_fully_connected_layer_8h_xhtml"><div class="ttname"><a href="_fully_connected_layer_8h.xhtml">FullyConnectedLayer.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aea27abcd3d58d627282320dfdd213596"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aea27abcd3d58d627282320dfdd213596">arm_compute::test::validation::asymm_int_mult</a></div><div class="ttdeci">int32_t asymm_int_mult(int32_t a, int32_t b)</div><div class="ttdoc">Multiplication of two integers. </div><div class="ttdef"><b>Definition:</b> <a href="_utils_quantized_asymm_8h_source.xhtml#l00044">UtilsQuantizedAsymm.h:44</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a91d8061f66e7f8bc56da91d965f04376"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">arm_compute::TensorShape::total_size_upper</a></div><div class="ttdeci">size_t total_size_upper(size_t dimension) const </div><div class="ttdoc">Collapses given dimension and above. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00167">TensorShape.h:167</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00159">Error.h:159</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_aec801fd424adad36e632d433eb113c01"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#aec801fd424adad36e632d433eb113c01">arm_compute::test::validation::reference::fully_connected_layer</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; fully_connected_layer(const SimpleTensor&lt; T &gt; &amp;src, const SimpleTensor&lt; T &gt; &amp;weights, const SimpleTensor&lt; TB &gt; &amp;bias, const TensorShape &amp;dst_shape)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_fully_connected_layer_8cpp_source.xhtml#l00152">FullyConnectedLayer.cpp:152</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
<div class="ttc" id="_utils_quantized_asymm_8h_xhtml"><div class="ttname"><a href="_utils_quantized_asymm_8h.xhtml">UtilsQuantizedAsymm.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a0f59f175e7682c7ed5f4ea30ef687834"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">arm_compute::Dimensions::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const </div><div class="ttdoc">Returns the effective dimensionality of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point&lt; T &gt; max(fixed_point&lt; T &gt; x, fixed_point&lt; T &gt; y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00902">FixedPoint.h:902</a></div></div>
<div class="ttc" id="_asymm_helpers_8h_xhtml"><div class="ttname"><a href="_asymm_helpers_8h.xhtml">AsymmHelpers.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::test::SimpleTensor::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Quantization info in case of asymmetric quantized type. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00312">SimpleTensor.h:312</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div>
<div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a35ccf2eb0c18a15feab2db98b307b78b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">arm_compute::test::SimpleTensor::fixed_point_position</a></div><div class="ttdeci">int fixed_point_position() const override</div><div class="ttdoc">Number of bits for the fractional part. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00306">SimpleTensor.h:306</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure &amp; src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
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